This paper contains the description of argumentation approach for the problem of inductive concept formation. It is proposed to use argumentation, based on defeasible reasoning with justification degrees, to improve the quality of classification models, obtained by generalization algorithms. The experiment’s results on both clear and noisy data are also presented. Keywords—Argumentation, justif...

Hierarchical concept learning constructs higher-level concepts using previously learned prerequisite concepts. We are working in an especially challenging context where only a small number of training instances for each concept are provided to the learning system. This limited instruction forces even the most skillful learner to make assumptions about the concept being taught—assumptions that c...

ion & concept Formation 23.92 3.46 Abstraction & concept formation –WM 24.49 3.29ion & concept formation –WM 24.49 3.29 Abstraction & concept formation RT 2125.10 565.45ion & concept formation RT 2125.10 565.45 Abstraction & concept formation WM RT 1492.40 413.93ion & concept formation WM RT 1492.40 413.93 Attention and working memory RT 425.00 95.38 Sensory-motor RT (Trial 1) 791.52 287.83 Sen...

The topic I’d like to talk to you about today is, I think, one of the oldest and most basic in cognition: how we learn from examples. As most famously pointed out by Hume, when we make a finite number of observations of an enduring phenomenon, there is no strictly logical (i.e., deductive) basis for forming any firm generalizations about it. Instead we must “induce,” that is, make educated gues...

Selective induction techniques perform poorly when the features are inappropriate for the target concept. One solution is to have the learning system construct new features automatically ; unfortunately feature construction is a diicult and poorly understood problem. In this paper we present a deenition of feature construction in concept learning, and ooer a framework for its study based on fou...

The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size. This paper asks whether good learning algorithms can be designed by maximizing their coverage. The paper extends a previous upper bound on the coverage of any Boolean concept learning algorithm and describes two algorithms|Multi-Balls and Large-Ball|whose coverage a...

This qualitative study examined concept formation of Latin prefixes in three students diagnosed with learning disabilities through a theme-based analysis. The study had two purposes. Conceptual processes underlying the learning of Latin prefixes were explored through dynamic assessment and Vygotsky’s stages of concept formation were empirically tested. There were five major findings in this stu...

In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to describe them. Existing concept learners require that this set of attributes be known in advance and presented in entirety before learning begins. Additionally, most systems do not possess mechanisms for altering the attribute set after concepts have been learned. Consequent...

This paper describes a framework that generates constructive induction schemes for the concept formation system COBWEB. The basis of this framework|context-dependent bias of multi-valued properties|provides a way for allowing COBWEB to deal with continuous and hierarchical property types as a special case of constructive induction. The constructive induction scheme does not introduce learning b...

Many concept formation systems construct disjoint-concept trees. However, a priori imposed tree structures may restrict the application of these systems in some domains. A joint concept formation scheme is thus proposed, which learns from observation, and constructs acyclic directed concept graphs (trees are a special case). We show that the joint concept formation system can avoid or alleviate...